Overview

Dataset statistics

Number of variables7
Number of observations660
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.2 KiB
Average record size in memory56.2 B

Variable types

NUM7

Reproduction

Analysis started2020-11-29 22:22:02.498119
Analysis finished2020-11-29 22:22:10.382035
Duration7.88 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Sl_No has unique values Unique
Total_visits_bank has 100 (15.2%) zeros Zeros
Total_visits_online has 144 (21.8%) zeros Zeros
Total_calls_made has 97 (14.7%) zeros Zeros

Variables

Sl_No
Real number (ℝ≥0)

UNIQUE

Distinct count660
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.5
Minimum1
Maximum660
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-11-29T16:22:10.437976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.95
Q1165.75
median330.5
Q3495.25
95-th percentile627.05
Maximum660
Range659
Interquartile range (IQR)329.5

Descriptive statistics

Standard deviation190.6698718
Coefficient of variation (CV)0.576913379
Kurtosis-1.2
Mean330.5
Median Absolute Deviation (MAD)165
Skewness0
Sum218130
Variance36355
2020-11-29T16:22:10.522292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
66010.2%
 
22610.2%
 
22410.2%
 
22310.2%
 
22210.2%
 
22110.2%
 
22010.2%
 
21910.2%
 
21810.2%
 
21710.2%
 
Other values (650)65098.5%
 
ValueCountFrequency (%) 
110.2%
 
210.2%
 
310.2%
 
410.2%
 
510.2%
 
ValueCountFrequency (%) 
66010.2%
 
65910.2%
 
65810.2%
 
65710.2%
 
65610.2%
 

Customer Key
Real number (ℝ≥0)

Distinct count655
Unique (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55141.44393939394
Minimum11265
Maximum99843
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-11-29T16:22:10.604599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum11265
5-th percentile15317.6
Q133825.25
median53874.5
Q377202.5
95-th percentile96301.45
Maximum99843
Range88578
Interquartile range (IQR)43377.25

Descriptive statistics

Standard deviation25627.7722
Coefficient of variation (CV)0.4647642566
Kurtosis-1.147577576
Mean55141.44394
Median Absolute Deviation (MAD)21533
Skewness0.0514619906
Sum36393353
Variance656782707.9
2020-11-29T16:22:10.680262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4743720.3%
 
3725220.3%
 
9793520.3%
 
9692920.3%
 
5070620.3%
 
7577510.2%
 
4367910.2%
 
3329510.2%
 
6791110.2%
 
9452910.2%
 
Other values (645)64597.7%
 
ValueCountFrequency (%) 
1126510.2%
 
1139810.2%
 
1141210.2%
 
1146610.2%
 
1156210.2%
 
ValueCountFrequency (%) 
9984310.2%
 
9959610.2%
 
9958910.2%
 
9947310.2%
 
9943710.2%
 

Avg_Credit_Limit
Real number (ℝ≥0)

Distinct count110
Unique (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34574.242424242424
Minimum3000
Maximum200000
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-11-29T16:22:10.763030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile6000
Q110000
median18000
Q348000
95-th percentile121100
Maximum200000
Range197000
Interquartile range (IQR)38000

Descriptive statistics

Standard deviation37625.4878
Coefficient of variation (CV)1.088251981
Kurtosis5.133842332
Mean34574.24242
Median Absolute Deviation (MAD)11000
Skewness2.202395623
Sum22819000
Variance1415677333
2020-11-29T16:22:10.848012image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8000355.3%
 
6000314.7%
 
9000284.2%
 
13000284.2%
 
10000263.9%
 
19000263.9%
 
7000243.6%
 
11000243.6%
 
18000233.5%
 
14000233.5%
 
Other values (100)39259.4%
 
ValueCountFrequency (%) 
300010.2%
 
5000213.2%
 
6000314.7%
 
7000243.6%
 
8000355.3%
 
ValueCountFrequency (%) 
20000010.2%
 
19500020.3%
 
18700010.2%
 
18600010.2%
 
18400010.2%
 

Total_Credit_Cards
Real number (ℝ≥0)

Distinct count10
Unique (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.706060606060606
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size5.2 KiB
2020-11-29T16:22:10.937781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.16783486
Coefficient of variation (CV)0.4606474589
Kurtosis-0.3697703016
Mean4.706060606
Median Absolute Deviation (MAD)1
Skewness0.1448789903
Sum3106
Variance4.699507978
2020-11-29T16:22:11.021486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
415122.9%
 
611717.7%
 
710115.3%
 
57411.2%
 
2649.7%
 
1598.9%
 
3538.0%
 
10192.9%
 
9111.7%
 
8111.7%
 
ValueCountFrequency (%) 
1598.9%
 
2649.7%
 
3538.0%
 
415122.9%
 
57411.2%
 
ValueCountFrequency (%) 
10192.9%
 
9111.7%
 
8111.7%
 
710115.3%
 
611717.7%
 

Total_visits_bank
Real number (ℝ≥0)

ZEROS

Distinct count6
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.403030303030303
Minimum0
Maximum5
Zeros100
Zeros (%)15.2%
Memory size5.2 KiB
2020-11-29T16:22:11.102541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.631812876
Coefficient of variation (CV)0.6790646267
Kurtosis-1.104274131
Mean2.403030303
Median Absolute Deviation (MAD)1
Skewness0.1418960148
Sum1586
Variance2.662813262
2020-11-29T16:22:11.181349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
215823.9%
 
111217.0%
 
310015.2%
 
010015.2%
 
59814.8%
 
49213.9%
 
ValueCountFrequency (%) 
010015.2%
 
111217.0%
 
215823.9%
 
310015.2%
 
49213.9%
 
ValueCountFrequency (%) 
59814.8%
 
49213.9%
 
310015.2%
 
215823.9%
 
111217.0%
 

Total_visits_online
Real number (ℝ≥0)

ZEROS

Distinct count16
Unique (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.606060606060606
Minimum0
Maximum15
Zeros144
Zeros (%)21.8%
Memory size5.2 KiB
2020-11-29T16:22:11.357099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9.05
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.93572412
Coefficient of variation (CV)1.12649879
Kurtosis5.739571572
Mean2.606060606
Median Absolute Deviation (MAD)1
Skewness2.225606714
Sum1720
Variance8.618476112
2020-11-29T16:22:11.441765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
218928.6%
 
014421.8%
 
110916.5%
 
46910.5%
 
5548.2%
 
3446.7%
 
15101.5%
 
771.1%
 
1260.9%
 
1060.9%
 
Other values (6)223.3%
 
ValueCountFrequency (%) 
014421.8%
 
110916.5%
 
218928.6%
 
3446.7%
 
46910.5%
 
ValueCountFrequency (%) 
15101.5%
 
1410.2%
 
1350.8%
 
1260.9%
 
1150.8%
 

Total_calls_made
Real number (ℝ≥0)

ZEROS

Distinct count11
Unique (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5833333333333335
Minimum0
Maximum10
Zeros97
Zeros (%)14.7%
Memory size5.2 KiB
2020-11-29T16:22:11.525691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.865316818
Coefficient of variation (CV)0.7996232979
Kurtosis-0.5182644359
Mean3.583333333
Median Absolute Deviation (MAD)2
Skewness0.6589053024
Sum2365
Variance8.210040465
2020-11-29T16:22:11.600425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
410816.4%
 
09714.7%
 
29113.8%
 
19013.6%
 
38312.6%
 
6395.9%
 
7355.3%
 
9324.8%
 
8304.5%
 
5294.4%
 
ValueCountFrequency (%) 
09714.7%
 
19013.6%
 
29113.8%
 
38312.6%
 
410816.4%
 
ValueCountFrequency (%) 
10263.9%
 
9324.8%
 
8304.5%
 
7355.3%
 
6395.9%
 

Interactions

2020-11-29T16:22:04.586882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:04.783254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:04.888984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.006300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.110702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.222323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.336141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.442407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.544918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.645839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.757139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.855734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:05.957260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.062392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.164118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.283389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.398249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.524323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.638164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.754207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.878411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:06.994215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.095461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.196560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.309284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.406777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.505893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.611273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.714227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:07.822475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.000063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.115568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.218291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.320929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.428706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.531968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.643287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.753079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.873435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:08.981064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.089694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.204081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.311948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.418096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.520199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.633691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.733974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.836675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:09.944462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-11-29T16:22:11.685496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-29T16:22:11.830477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-29T16:22:11.971660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-29T16:22:12.112437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-29T16:22:10.130213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-29T16:22:10.288818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Sl_NoCustomer KeyAvg_Credit_LimitTotal_Credit_CardsTotal_visits_bankTotal_visits_onlineTotal_calls_made
01870731000002110
12384145000030109
2317341500007134
3440496300005114
454743710000060123
5658634200003018
674837010000050112
7837376150003011
898249050002022
9104477030004017

Last rows

Sl_NoCustomer KeyAvg_Credit_LimitTotal_Credit_CardsTotal_visits_bankTotal_visits_onlineTotal_calls_made
65065178996195000101122
6516527840413200091122
652653285251560008180
6536545182695000100151
6546556575017200010191
6556565110899000101100
6566576073284000101132
657658538341450008191
65865980655172000101150
6596608015016700090122